@article {4568,
	title = {Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics.},
	journal = {Elife},
	volume = {12},
	year = {2024},
	month = {2024 Sep 06},
	abstract = {<p>Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/ noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation{\textquoteright}s fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method{\textquoteright}s specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.</p>},
	keywords = {Brain, Electrocorticography, Electroencephalography, Humans, Signal Processing, Computer-Assisted},
	issn = {2050-084X},
	doi = {10.7554/eLife.91605},
	author = {Cho, Hohyun and Adamek, Markus and Willie, Jon T and Brunner, Peter}
}
@article {4583,
	title = {A novel theta-controlled vibrotactile brain-computer interface to treat chronic pain: a pilot study.},
	journal = {Sci Rep},
	volume = {14},
	year = {2024},
	month = {2024 Feb 10},
	pages = {3433},
	abstract = {<p>Limitations in chronic pain therapies necessitate novel interventions that are effective, accessible, and safe. Brain-computer interfaces (BCIs) provide a promising modality for targeting neuropathology underlying chronic pain by converting recorded neural activity into perceivable outputs. Recent evidence suggests that increased frontal theta power (4-7~Hz) reflects pain relief from chronic and acute pain. Further studies have suggested that vibrotactile stimulation decreases pain intensity in experimental and clinical models. This longitudinal, non-randomized, open-label pilot study{\textquoteright}s objective was to reinforce frontal theta activity in six patients with chronic upper extremity pain using a novel vibrotactile neurofeedback BCI system. Patients increased their BCI performance, reflecting thought-driven control of neurofeedback, and showed a significant decrease in pain severity (1.29 {\textpm} 0.25 MAD, p = 0.03, q = 0.05) and pain interference (1.79 {\textpm} 1.10 MAD p = 0.03, q = 0.05) scores without any adverse events. Pain relief significantly correlated with frontal theta modulation. These findings highlight the potential of BCI-mediated cortico-sensory coupling of frontal theta with vibrotactile stimulation for alleviating chronic pain.</p>},
	keywords = {brain-computer interfaces, Chronic Pain, Electroencephalography, Humans, Longitudinal Studies, Neurofeedback, Non-Randomized Controlled Trials as Topic, Pilot Projects},
	issn = {2045-2322},
	doi = {10.1038/s41598-024-53261-3},
	author = {Demarest, Phillip and Rustamov, Nabi and Swift, James and Xie, Tao and Adamek, Markus and Cho, Hohyun and Wilson, Elizabeth and Han, Zhuangyu and Belsten, Alexander and Luczak, Nicholas and Brunner, Peter and Haroutounian, Simon and Leuthardt, Eric C}
}
